Our algorithm guarantees provable exact unlearning with probabilistic
runtime guarantees, whereas [1]} give algorithms with deterministic runtime and provide only an approximate \((\epsilon , \delta )\) -DP based unlearning guarantee – the \(\delta \)  can be interpreted as probability of the failure event in Monte-Carlo guarantees.
To handle these discrepancies when comparing, our stated runtime is the in-expecatation runtime. For a fixed runtime, we will look at regimes of \(\epsilon \)  and \(\delta \) , when the accuracy guarantee of [1]} is smaller than ours. We remind that a large \(\epsilon ,\delta \)  means a weaker unlearning criterion.
We will see that with same runtime, the accuracy of [1]} is smaller than ours in the regime when their unlearning parameters and hence the notion, is rather weak.
